Find Academic Supervisors

• Deep Learning
• Generative AI
• Graph Machine Learning
• +3 more

Gisele Miranda is a SciLifeLab Fellow at the KTH Royal Institute of Technology , specializing in advanced machine learning applications for biological systems. Her work integrates generative AI , deep learning , and multimodal data analysis to decode cellular networks and tissue architecture. Research focuses include: Real-time prediction of biological dynamics using deep convolutional neural networks Modeling spatial spread of infectious diseases through mobility patterns Development of graph-based machine learning techniques Multi-modal profiling for enhanced disease diagnosis Current projects involve simulating cellular microenvironments by integrating microscopy images and omics data. Her group actively hires PhDs and Postdocs for these initiatives. Scientific accolades include the SciLifeLab Fellowship, and her publications demonstrate expertise in both computational methods and biomedical applications.

• Theoretical Computer Science
• Database Theory
• Formal Logic
• +5 more

Nicole Schweikardt is a Professor at the Institute of Computer Science within Humboldt University of Berlin . Her research focuses on Theoretical Computer Science , particularly in Database Theory , Formal Logic , and Algorithmic Meta-Theorems . Academic Rank: Professor Contact: schweikn@informatik.hu-berlin.de Research Interests : Nicole investigates logical characterizations of database query languages, algorithmic meta-theorems for sparse graphs, and efficient enumeration techniques. Her work bridges formal logic, computational complexity, and practical database systems. Scientific Awards : 2018 ACM PODS Alberto O. Mendelzon Test-of-Time Award Recent Article Trends : Nicole's recent publications emphasize schema matching , spanner evaluation , first-order logic extensions , and query enumeration . Her work spans theoretical foundations (e.g., counting quantifiers, Hanf normal forms) and practical applications (e.g., event stream analysis, document compression).

• Semiotics
• Complex Adaptive Systems
• Human-Environment Systems
• +7 more

Athanasios Votsis is an Assistant Professor at the Department of Governance and Technology for Sustainability, University of Twente. His interdisciplinary work bridges semiotics, computational methods, and climate resilience. Education: PhD in Human Geography (University of Helsinki), Master's in Urbanism (Virginia Commonwealth University) Research Interests include: Semiotics (culture, AI, urbanism) Computational methods (cellular automata, agent-based models) Urbanism & Spatial Planning (bottom-up urbanism, resilience trajectories) Spatial Economics (housing markets, natural risks) Climate Adaptation (heatwaves, flooding, aviation energy sectors) AI & Robotics (spatial cognition, human-machine interaction) Recent Publications focus on climate hazards in aviation, energy autarky frameworks, and computational tools for urban planning. Key collaborations include work with the UN ESCAP-APDIM and the Finnish Environment Institute. Scientific Awards : Decentral Educational Award (2023) Supervisory Work covers diverse topics like circular economy in cities, renewable energy policy modeling, and climate-proof spatial planning. He is affiliated with international networks including 4TU Resilience Engineering, ICOMOS, and iEMSs.

Francesco RanzatoView profile

Professor

• Abstract Interpretation
• Programming Languages
• Formal Methods
• +4 more

Francesco Ranzato is a Full Professor in the Department of Mathematics at the University of Padova, Italy, where he conducts research in theoretical computer science with a focus on abstract interpretation, programming languages, and formal methods. His academic career spans over three decades with continuous contributions to the field, as evidenced by his extensive publication record from 1994 through 2025. Professor Ranzato's research interests center on abstract interpretation theory and its applications to program analysis and verification. In recent years, he has expanded his work to include machine learning verification, particularly focusing on robustness certification of classifiers using abstract interpretation techniques. His work bridges theoretical foundations with practical applications, as demonstrated by multiple open-source software artifacts available on GitHub. His publication record shows a clear evolution from foundational work in abstract domains and completeness properties to practical algorithms for simulation and model checking, and most recently to applications in machine learning security. The 15 most recent publications reveal a strong focus on verifying properties of machine learning models, with particular attention to decision trees, support vector machines, and k-Nearest Neighbors classifiers. Among his notable recognitions are the POPL Distinguished Paper Award, LICS 2021 Best Paper Award, Microsoft Research SEIF award, Amazon Research Award, and the WhatsApp Privacy Aware Program Analysis Award. These awards highlight the significance and impact of his contributions to programming languages and formal methods research. Professor Ranzato maintains active research collaborations, as evidenced by his co-authored publications, and has successfully secured research funding from both academic and industry sources. His work with students and junior researchers is reflected in the numerous joint publications and the development of practical tools for abstract interpretation and machine learning verification. His research group operates through the Department of Mathematics at the University of Padova, with a strong presence in international research communities as shown by his editorial roles (e.g., editing SAS 2017 proceedings) and regular participation in top-tier conferences in programming languages and formal methods.

• Quantum Computing
• Data Science
• Social Network Analysis
• +4 more

George Km is a Professor of Computer Science at Oklahoma State University, affiliated with the Department of Computer Science since 1994. He holds a Ph.D. in Mathematics from Stony Brook University. His research focuses on quantum computing, big data analytics, social media analysis, and algorithm design. He has led numerous grants, including projects on quantum sensor data prediction, big data education, and Hadoop optimization. Notable contributions include work on quantum circuit algorithms, predictive analytics using Twitter data, and entropy-based veracity models. Teaching responsibilities include courses such as Formal Language Theory, Quantum Algorithms, and Numerical Methods. He has designed advanced special topics courses in quantum computing and big data. His work integrates theoretical concepts with practical applications in finance, healthcare, and social networks. Grants funded by the National Science Foundation (CISE) and industry partners demonstrate his impact in computational science and education. Publications span quantum automata, machine learning for financial modeling, and data reduction techniques. His research emphasizes interdisciplinary approaches, addressing challenges in algorithmic efficiency, data veracity, and computational infrastructure optimization. He advises on graduate courses and contributes to curriculum development in emerging technologies.

• Quantum Computing
• Machine Learning
• Computational Neuroscience
• +3 more

Johnson Thomas is a Professor in the Department of Computer Science at Oklahoma State University, with affiliations in both the Stillwater and Tulsa campuses. His research focuses on Quantum Computing, Machine Learning, and Computational Neuroscience, with notable work on quantum circuit optimization and spiking neural networks. He holds degrees from the University of Reading (PhD, 1995), University of Edinburgh (MSc, 1983), and University of Wales (BSc, 1982). His teaching spans advanced topics in databases, quantum computing, and programming languages. Recent courses include *Quantum Computing*, *Advanced Topics in Information Systems*, and *Discrete Mathematics for Computer Science*. He has also advised doctoral students and led research in distributed systems and security. Research interests include quantum algorithms, neurocomputational models, and causal inference. His work bridges theoretical foundations with practical applications, such as ROS security frameworks and NIRS-based forage quality prediction. Over 20 funded projects highlight his expertise in big data, autonomous systems, and sensor networks. Publications emphasize quantum circuit design, medical ML interpretation, and computational neuroscience. Collaborations include interdisciplinary projects with healthcare and agricultural sectors. His contributions advance both theoretical computer science and applied technologies.

Rafael FrongilloView profile

Associate Professor

• Theoretical Machine Learning
• Economics
• Information Elicitation
• +5 more

Rafael Frongillo is an Associate Professor of Computer Science at the University of Colorado Boulder and holds the Roubos Engineering Endowed Faculty Fellowship. His research bridges theoretical machine learning and economics, focusing on information elicitation, crowdsourcing, and mechanisms involving monetary incentives. He employs techniques from convex analysis, game theory, and statistics to address challenges in prediction markets, peer prediction, and algorithmic decision-making. Frongillo earned his Ph.D. in Computer Science from UC Berkeley in 2013, advised by Christos Papadimitriou, and held postdoctoral positions at Harvard University’s Center for Research on Computation and Society and Microsoft Research New York. Education: Ph.D. in Computer Science, UC Berkeley (2013), advised by Christos Papadimitriou Postdoctoral Researcher, Harvard University (2013–?), Microsoft Research New York (2013–?) Research Interests: Frongillo’s work emphasizes the intersection of machine learning and economics, particularly in designing systems that incentivize truthful information sharing. Key themes include: Information elicitation via scoring rules and prediction markets Algorithmic game theory for crowdsourcing and peer prediction Optimization techniques for loss functions and statistical consistency Computational aspects of economic mechanisms Awards & Fellowships: Roubos Engineering Endowed Faculty Fellow NDSEG Fellowship (2013) Grants & Advising: While specific grants are not detailed, his research has been supported by fellowships and institutional endowments. No formal advisee names are listed in the provided texts. Labs & Collaborations: Active in interdisciplinary collaborations at CU Boulder and prior institutions, though specific lab affiliations are not mentioned.

Fabio SomenziView profile

Professor

• Formal Verification
• Model Checking
• Reinforcement Learning
• +3 more

Dr. Fabio Somenzi is a Professor in the Department of Electrical, Computer & Energy Engineering at the University of Colorado Boulder. His research focuses on formal verification, model checking, decision procedures, and reinforcement learning. He has contributed significantly to the integration of formal methods with reinforcement learning, particularly in handling omega-regular objectives, multi-agent systems, and safety-critical applications. His recent work emphasizes combining reinforcement learning with formal specifications to ensure correctness and optimize decision-making under uncertainty. He has developed tools like Mungojerrie, which addresses model-free reinforcement learning for linear-time objectives. His research spans theoretical foundations, algorithm design, and practical implementations, with a strong emphasis on modular and compositional approaches. Key themes in his publications include policy synthesis for temporal logic objectives, reward shaping for formal specifications, and compositional methods for large-scale systems. His work bridges computer science, control theory, and artificial intelligence, addressing challenges in automated reasoning and decision processes. No scientific awards or grants are explicitly mentioned in the provided information. Dr. Somenzi collaborates on projects involving formal verification of analog circuits, stochastic systems, and abstract interpretation techniques.

• Software Security Engineering
• Intelligent Systems
• Interdisciplinary Research
• +4 more

Dr. Abdulrazaq Abba is a Lecturer at the Department of Computer Science and Digital Technology (CDT), University of East London. He is affiliated with the School of Architecture Computing and Engineering. His research focuses on software security engineering, intelligent systems (including serious games), and interdisciplinary applications of computing. He teaches courses such as Software Development, Advanced Programming, Data Structures and Algorithms, and Computer Security at both undergraduate and postgraduate levels. Abba’s research emphasizes leveraging computing to address real-world challenges. In software security, he explores techniques to enhance system trustworthiness across development lifecycle stages. His work on intelligent systems integrates learning and adaptive behaviors for applications in education, healthcare, and defense. He actively collaborates with industry and academia and supervises PhD candidates in computing-related fields. Abba is part of research groups including the Intelligent Technologies Research Group and Smart City Research Group. His publications span IoT meteorological systems, fall detection algorithms, healthcare supply chain security, and formal methods for temporal reasoning. He remains open to interdisciplinary collaborations and industry partnerships.

Vladik KreinovichView profile

Professor

• Uncertainty Management
• Interval Computations
• Fuzzy Logic
• +7 more

Professor Vladik Kreinovich is a renowned academic in the Department of Computer Science at the University of Texas at El Paso (UTEP), part of the College of Science. His primary research focuses on uncertainty management, interval computations, fuzzy logic, and decision-making under uncertainty. He holds a Doctor Honoris Causa from Ostrava University and is a Fellow of the International Fuzzy Systems Association (IFSA) and the Mexican Society for Artificial Intelligence (SMIA). His academic journey includes a B.S. and M.S. from St. Petersburg University and a Ph.D. from the Institute of Mathematics in Novosibirsk, Russia. Dr. Kreinovich has authored over 1,500 publications, with notable contributions to interval analysis, computational intelligence, and interdisciplinary applications in geoscience and environmental science. Awards include the K. S. Fu Award, Meritorious Service Award from IEEE, and the El Paso Energy Foundation Faculty Achievement Award. He serves on editorial boards for journals like Reliable Computing and International Journal of Approximate Reasoning . His teaching spans automata theory, formal languages, and advanced topics in computing paradigms. Grants include an AT&T Fellowship (2021–2028), and he actively contributes to conferences such as IFSA, EUSFLAT, and NAFIPS. His work bridges theoretical foundations with practical applications, emphasizing robust algorithms for uncertain data and interdisciplinary collaboration.

Vladimir KulyukinView profile

Associate Professor

• Artificial Intelligence
• Computer Vision
• Robotics
• +3 more

Vladimir Kulyukin is an Associate Professor in the Department of Computer Science at Utah State University. His research spans theoretical computer science, artificial intelligence, and robotics, with notable contributions to computability theory, bio-inspired computing, and assistive technologies for visually impaired individuals. He has pioneered work in non-invasive electronic beehive monitoring using machine learning, computer vision, and sensor networks, addressing challenges in precision agriculture and environmental science. Key research interests include formal methods in computability, neural network capabilities, and the intersection of robotics with human-centric applications. His work in assistive technologies includes innovations in blind navigation systems, mobile barcode scanning, and verbal route guidance for independent shopping. Recent articles highlight advancements in computability theory (e.g., universality theorems, primitive recursive functions), applications of YOLO networks for bee traffic analysis, and fusion of multimodal datasets for hive health monitoring. His interdisciplinary approach bridges theoretical foundations with practical implementations in agriculture, education, and accessibility. Notable projects include the development of BeePIV for bee traffic measurement, mobile PIV tools for fluid mechanics education, and the Shoptalk system for blind grocery shopping. These contributions underscore his commitment to both foundational research and real-world impact in STEM fields.

• Formal Methods
• Model Checking
• Automata Theory
• +5 more

Mahesh Viswanathan is a Professor and Associate Head at the Siebel School of Computing and Data Science at the University of Illinois. His primary research focuses on formal methods, automata theory, and model checking, with applications in computer security, runtime verification, and cyber-physical systems. He received the NSF CAREER Award in 2005 for his contributions to these fields. His research explores: Foundations of automata theory and formal verification Runtime assurance mechanisms for safety-critical systems Concurrency analysis and race detection algorithms Differential privacy and security protocol verification Theoretical aspects of real-time and hybrid systems Viswanathan's recent publications demonstrate a strong focus on developing theoretically-grounded verification techniques for practical applications. His work frequently combines formal methods with machine learning approaches to solve challenging problems in program verification and system security. Awards and Honors: NSF CAREER Award (2005)

Luis M. RochaView profile

Professor

• Complex Networks and Systems
• Computational and Systems Biology
• Computational Intelligence
• +1 more

Luis M. Rocha is the George J. Klir Professor of Systems Science at Binghamton University's Thomas J. Watson College of Engineering and Applied Science, leading the Complex Adaptive Systems and Computational Intelligence (CASCI) lab. He directs the Center for Social and Biomedical Complexity (joint with Indiana University) and previously led the NSF-NRT Interdisciplinary Training Program in Complex Networks and Systems. His academic career includes roles as Professor of Informatics at Indiana University and visiting positions at institutions like Aix-Marseille University and the Champalimaud Foundation. **Education**: PhD in Systems Science (Binghamton University, 1997); MEng and BEng in Engineering (Instituto Superior Técnico, Lisbon). **Research Interests**: Focus on complex networks, computational biology, and biomedical informatics. His work spans drug interaction analysis, social media health monitoring, and theoretical network science. **Awards**: Fulbright Scholar, Indiana University Teaching Excellence Awards (2006, 2015). **Key Contributions**: Developed CANA computational tools for Boolean networks, pioneered social media mining for health signals, and organized major conferences like Alife X and ECAL 2007. His lab (CASCI) bridges computational methods with biomedical and social complexity challenges. **Grants & Projects**: NSF-funded training programs, collaborations on digital twins in precision medicine, and cross-disciplinary initiatives in systems science.

• Optimization
• Parallel Algorithms
• Computer Science Education
• +3 more

John Glick, PhD, is a Professor of Computer Science at the University of San Diego since 1993. He teaches computer science and mathematics courses, including Assembly Language Programming, Operating Systems, Automata Theory, and an honors course in computer ethics. His research focuses on optimization, parallel algorithms, and computer science education pedagogy. He holds a PhD and MS in Computer Science from the University of Minnesota, and MS and BS degrees in Chemical Engineering from Northwestern University and the University of Kansas, respectively. Education: PhD, University of Minnesota, Computer Science MS, University of Minnesota, Computer Science MS, Northwestern University, Chemical Engineering BS, University of Kansas, Chemical Engineering Research & Teaching: Glick’s work emphasizes active learning methodologies such as Peer Instruction, with publications exploring student learning outcomes in computing education. He has contributed to discussions on academic policies like tenure and reappointment, and the unique challenges faced by Catholic university faculty. His interdisciplinary background bridges computer science and mathematics education. Advising & Grants: No specific advising or grant details are listed in the profile. However, his teaching roles imply involvement in student advising. Collaborations with colleagues on pedagogical research suggest potential grant-related activities not explicitly detailed here. Labs/Teams: No formal lab or research team affiliations are mentioned in the provided text.

• AI in Science
• Environmental Sciences
• Natural Language Processing
• +4 more

Dr. Martin Schüle is a Researcher at the Zurich University of Applied Sciences (ZHAW), specifically within the Institute of Computational Life Sciences at the ZHAW School of Life Sciences and Facility Management. He leads the 'Head Research AI & Computational Environment Focus' initiative and specializes in AI applications across environmental sciences, financial markets, and natural language processing. His work bridges theoretical computer science with real-world challenges, including optimizing agricultural practices and analyzing non-financial corporate communication. Research interests span AI ethics, neural networks, and nonlinear dynamics, with a focus on interdisciplinary projects like predicting investor behavior and improving fertilizer efficiency. He has contributed to over 15 peer-reviewed articles and conference papers, often as a principal investigator or co-author in collaborative international teams. Projects include leading efforts on optimal fertilizer application, greenwashing detection in corporate ESG reporting, and real-time agricultural decision systems. Active in networks such as the Deutsche Physikalische Gesellschaft and collaborates with institutions like Paris 1 Sorbonne. His articles explore foundational AI topics (e.g., large language model semantics) and applied domains (e.g., bond market sentiment analysis). He has presented at major venues like the International Symposium on Nonlinear Theory and its Applications (NOLTA), addressing synchronization in cellular automata and hybrid deep learning for environmental forecasting. While no scientific awards are explicitly listed, his extensive project portfolio highlights impactful contributions to computational science and policy-relevant research in sustainability and finance.